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Shape Boundary Tracking with Hidden Markov Models

  • Terry Caelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

This paper considers a Hidden Markov Model (HMM) for shape boundary generating which can be trained to be consistent with human expert performance on such tasks. That is, shapes are defined by sequences of “shape states” each of which has a probability distribution of expected image features (feature “symbols”). The tracking procedure uses a generalization of the Viterbi method by replacing its “best-first” search by “beam-search” so allowing the procedure to consider less likely features as well in the search for optimal state sequences. Results point to the benefits of such systems as an aide for experts in depiction shape boundaries as is required, for example, in Cartography.

Keywords

Hidden Markov Models symbolic descriptions of boundaries predicting human performance Viterbi Search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Terry Caelli
    • 1
  1. 1.Department of Computing Science Research Institute for Multimedia Systems (RIMS)The University of AlbertaEdmontonCanada

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